Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line, or a photograph of a room in a factory. Often sophisticated process management and control software examines the incoming data, produces status reports, and, in many cases, responds by sending commands to actuators/controllers that adjust the operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailor the process (e.g., specify new set points) in response to varying external conditions (including costs of raw materials), detect an inefficient/non-optimal operating condition and/or impending equipment failure, and take remedial actions such as move equipment into and out of service as required.
Typical industrial processes are extremely complex and receive substantially greater volumes of information than any human could possibly digest in its raw form. By way of example, it is not unheard of to have thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling aspects of a multi-stage process within an industrial plant. These sensors are of varied type and report on varied characteristics of the process. Their outputs are similarly varied in the meaning of their measurements, in the amount of data sent for each measurement, and in the frequency of their measurements. As regards the latter, for accuracy and to enable quick response, some of these sensors/control elements take one or more measurements every second. When multiplied by thousands of sensors/control elements, this results in so much data flowing into the process control system that sophisticated data management and process visualization techniques are required.
Highly advanced human-machine interface/process visualization systems exist today that are linked to data sources such as the above-described sensors and controllers. Such systems acquire and digest (e.g., filter) the process data described above. The digested process data in-turn drives a graphical display rendered by a human machine interface. An example of such system is the well-known Wonderware INTOUCH® human-machine interface (HMI) software system for visualizing and controlling a wide variety of industrial processes. An INTOUCH HMI process visualization application includes a set of graphical views of a particular process. Each view, in turn, comprises one or more graphical elements. The graphical elements are “animated” in the sense that their display state changes over time in response to associated/linked data sources. For example, a view of a refining process potentially includes a tank graphical element. The tank graphical element has a visual indicator showing the level of a liquid contained within the tank, and the level indicator of the graphical element rises and falls in response to a steam of data supplied by a tank level sensor indicative of the liquid level within the tank. Animated graphical images driven by constantly changing process data values within data streams, of which the tank level indicator is only one example, are considerably easier for a human observer to comprehend than a steam of numbers. For this reason process visualization systems, such as INTOUCH, have become essential components of supervisory process control and manufacturing information systems.
Management of supervisory process control visualization applications based upon the aforementioned visualization systems can be a monumental task. Configuring such applications includes specifying graphical/animated images within a graphical interface window and linking such images to data sources. A number of tools significantly reduce the effort involved in producing such interfaces. For example, bitmaps and animation scripts that drive view/appearance characteristics of a depicted graphical element are provided by graphical element vendors and maintained within a graphical element library. Developers of particular visualization applications, through known HMI configuration tools (e.g., Wonderware's Window Maker utility within the INTOUCH software suite) need only specify a set of graphical elements (copied from a graphical element library) and their associated links to data sources. Such tools are of substantial value to HMI process visualization application developers. In fact these useful development aids facilitates application developers creating extensive, elaborate, and complex user interfaces comprising large numbers of animated graphical elements. However, keeping track of all the various graphical elements and their associated data sources within a complex application can be overwhelming.